The world of retail is vastly different than it was a year ago, and retailers are more challenged than ever to remain competitive. FMCG retailers understand that accurate demand forecasting across all categories, including fresh, is key to maintaining their competitive edge, staying profitable, and increasing sales – all of which are “reflectors” of today’s shopper demand.
While there is no shortage of forecasting solutions, there is scarcity of solutions that take into full consideration the complexity and reach of today’s FMCG retail supply chain.
In this series, I’ll explore the key questions and the underlying issues that must be addressed when evaluating your current demand forecasting solution and what to do if that system comes up lacking.
The Question: Are demand planners working for the system or is it working for them?
If your demand planners are struggling, it’s likely that data quality is a problem. Take out-of-stocks, for example. When they occur, you’ve lost sales for a given item for a period. Your planners bear the brunt of this because they must compensate and manually input data into the forecasting system.
How about new product introduction? With most forecasting solutions, planners must manually indicate to the forecast engine how to link this new item to a previous “like’’ item so that they can use that item’s history to forecast. And there are many types of “new items.” It might be a brand-new item, a substitution of an existing item, or a variant of an existing item. These all require substantial manual intervention.
Seasonality is another manual time sink for planners. Many forecasting tools have features that attempt to help with this, but they are not fully automated. Add to this, complicating factors like new store openings, which can have a significant impact on forecasting, and things become even more manual and error prone.
A recent survey conducted by EnsembleIQ Research revealed that 52% of retail supply chain executives say they spend too much time crunching data. This lost time and energy was the second greatest catalyst for new investment SCM solutions behind availability (which I’ll be covering in a later part of this series).
To manage and successfully analyze the massive amounts of data retailers face, AI-based forecasting with machine learning has become the new standard for retail demand forecasting. With AI-based systems, there’s no need for retailers to hire additional data scientists, which are a scarce (and costly) resource. Instead, the system serves as an automated data scientist for your data, with new levels of information, alerts and insights.
AI isn’t just for the largest retailers, either. The emergence of new Software as a Service (SaaS) solutions makes AI a practical reality for retailers of all sizes, enabling them to take advantage of the power of machine learning and free up their supply chain managers for more strategic work.
Simply put, there are many activities for which maintaining good data is critical for the forecast engine to perform correctly. Ensure the demand forecasting solution you choose removes the manual intervention present in many systems today and has advanced data-cleansing capabilities, that learn.
Don’t just take my word for it
“You have to be able to anticipate the precise expectations of each customer and the exact needs of each point of sale in order to meet demand and to deliver maximum fluidity and reliability. It is essential to manage the supply chain from end to end, with great flexibility and responsiveness,” said Jean-Michel Balaguer, STIME President, CTO of Groupement Les Mousquetaires, Intermarché entrepreneur, and Région Centre Est administrator. “This is exactly what the AI-based technologies from Symphony RetailAI bring us.”
Read the full survey report from Ensemble IQ: Strengthening the Retail Supply Chain
Read part 2 in the series: Are your demand forecasting systems connecting all categories across the store?
Read part 3 in the series: Why demand forecasting must be effective with all channels to be effective with any